GesTEApp: A Pilot Study on an Expert Web-Based System that

Integrates Gestural Analytics and a Hybrid Recommendation System to

Support the Early Detection of ASD in Children

Maria Gabriela Mej

ıa-Trujillo

, Faiber Orlando Camelo-Romero

Helio Henry Ram

ırez-Ar

evalo

and Miguel Alfonso Feij

oo-Garc

ıa

Program of Systems Engineering, Universidad El Bosque, Bogot

a, Colombia

Keywords:

Healthcare Information Systems, Recommendation Systems, Gestural Analysis, Machine Learning.

Abstract:

Autism Spectrum Disorder is a neurological condition that affects 1 in 160 children worldwide. To date, this

disorder does not yet have a standardized cure, and not being treated early can affect the child’s quality of

life and their relatives. There are currently different traditional tools for detecting Autism Spectrum Disor-

der, such as questionnaires and checklists— standardized methods worldwide, such as using M-CHAT-R/F

and Q-CHAT. We present GesTEApp as a web-based expert system that integrates gestural analytics and sup-

ports Healthcare Professionals in their medical decision-making process on the early detection of this disorder

in children. GesTEApp implements a Hybrid Recommendation System with Face Recognition models and

Linear Kernel, which capture and analyze children’s facial expressions, seeking to support Healthcare Profes-

sionals in detecting Autism Spectrum Disorder. We evaluated this tool following a pilot study and reported

the ﬁndings and results considering Healthcare Professionals’ perceptions, basing our analysis on (1-5) Lik-

ert Scales and their feedback regarding their experience interacting with GesTEApp. Preliminary, the tool

reduced detection times by 36% compared to traditional tools. Also, our preliminary results suggest that

GesTEApp is a user-centered web-based application that satisfactorily supports Healthcare Professionals in

detecting Autism Spectrum Disorder in children.

1 INTRODUCTION

Autism Spectrum Disorder (ASD) is a neurodevelop-

mental disorder characterized by causing persistent

deﬁciencies in communication and social interaction

in various contexts (Lord et al., 2020). It manifests

in socio-emotional reciprocity from an abnormal so-

cial approach, shared affections, and social interac-

tion. Moreover, impairments in nonverbal commu-

nicative behaviors, abnormalities in eye contact and

body language, as in comprehension and use of par-

ticular gestures, are also manifested (Spitzer et al.,

1980).

According to the DSM-V (i.e., Diagnostic and

Statistical Manual of Mental Disorders), the symp-

toms must be present in the ﬁrst phases of the person’s

developmental period but may fully manifest until the

https://orcid.org/0009-0002-3333-5483

https://orcid.org/0009-0009-9580-5621

https://orcid.org/0000-0001-6420-5687

https://orcid.org/0000-0001-5648-9966

social demand exceeds the limited capacities (Edition

et al., 2013). Although there is no standardized cure

for ASD to date, different studies have shown that

early detection of the disorder and initiation of treat-

ment in time to maximize the individual’s functional

independence can improve the quality of life of the

person and their relatives (Lai et al., 2014).

Numerous screening tools such as question-

naires and checklists have been implemented world-

wide regarding ASD — screening tools such as Q-

CHAT (Roman-Urrestarazu et al., 2021), M-CHAT

(Dumont-Mathieu and Fein, 2005), M-CHAT R/F

(Coelho-Medeiros et al., 2019), among others. These

tools have helped to diagnose ASD before two years

of age, presumingly. Despite this, the criteria for mak-

ing this diagnosis in children have yet to be well-

established (Goin-Kochel et al., 2006). Generically,

the tools commonly used to detect ASD are based on

questionnaires made to parents and Healthcare Pro-

fessionals (HcP). Unfortunately, this shows the lack

of resources of technological tools to support the de-

tection of ASD and related disorders.

170

Mejía-Trujillo, M., Camelo-Romero, F., Ramírez-Arévalo, H. and Feijóo-García, M.

GesTEApp: A Pilot Study on an Expert Web-Based System that Integrates Gestural Analytics and a Hybrid Recommendation System to Support the Early Detection of ASD in Children.

DOI: 10.5220/0011957500003476

In Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2023), pages 170-177

ISBN: 978-989-758-645-3; ISSN: 2184-4984

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Figure 1: Conceptual design of GesTEApp.

It is estimated that approximately 16% of South

America’s population under 15 years of age suffer

from some developmental disorder. However, the

prevalence of this disorder has not yet been estab-

lished to date (of Health, 2015).

We present GesTEApp as a web-based expert sys-

tem based on a recommendation system that supports

the detection and analysis of behavioral patterns of

patients (i.e., children) and ﬁnds matches with the

most representative of ASD, stipulated by medical lit-

erature. Hybrid Recommendation Systems combine

different recommendation systems to produce outputs

to complement their features and make better conjoint

recommendations (Seth and Sharaff, 2022). There-

fore, GesTEApp recommends to the user (i.e., HcP) a

customizable report to support their medical decision-

making processes on carrying out specialized or deep-

ened tests, as to consider the referral to specialists to

obtain an ofﬁcial diagnosis regarding ASD more ef-

fective, efﬁcient, and objectively — through the inte-

gration and implementation of a hybrid recommenda-

tion system.

We intend GesTEApp to be implemented in the

ﬁrst stage of the medical process— in the manda-

tory pediatric controls. Hence, we look forward to

GesTEApp supporting the detection of the ﬁrst signs

of the disorder from the ﬁrst medical approach and

avoiding long waiting times given by the traditional

screening tools for the ﬁrst screening evaluation— the

waiting times depend on the region or country where

the ASD test is held (Lord et al., 2020).

Our work contributes to Healthcare Information

Systems and Artiﬁcial Intelligence literature— the so-

lution implicates the relation between a hybrid recom-

mendation system and gestural analysis through arti-

ﬁcial vision. As a result, GesTEApp intends to re-

duce ASD detection times compared to the ordinary.

Additionally, we look forward to the web-based ap-

plication being accurate with its results concerning

the recommendation made following the evaluation

standards of Machine Learning models (i.e., accuracy

>= 75%), implying a low error rate and fewer false

positive cases. Also, with this healthcare-based so-

lution, we intended to increase the end user’s satis-

faction (i.e., HcP) regarding the support to ASD de-

tection or related medical decision-making processes.

Thus, we ask the following questions: How can the

medical procedure of detection of ASD be quantiﬁed

and supported by artiﬁcial vision? How can a techno-

logical tool, through an expert system based on a rec-

ommendation system that implements gestural analy-

sis, support HcPs in the early detection of ASD, re-

duces procedural times, and increase detection accu-

racy?

We evaluated this tool following a pilot study and

reported the preliminary ﬁndings and results consid-

ering HcPs’ perceptions, basing our analysis on (1-5)

Likert Scales (Joshi et al., 2015) regarding their ex-

perience interacting with GesTEApp. This approach

uses e-health-based (Eysenbach et al., 2001), artiﬁcial

intelligence, emotional intelligence (Prentice et al.,

2020),seeking to support medical decision-making

processes for HcP with the early detection of ASD.

GesTEApp: A Pilot Study on an Expert Web-Based System that Integrates Gestural Analytics and a Hybrid Recommendation System to

Support the Early Detection of ASD in Children

171

2 GesTEApp: DESCRIPTION

We present GesTEApp as a web-based approach that

integrates a hybrid recommendation system that, with

constant automated learning, supports the detection

process and analysis of behavioral patterns of patients

(i.e., children aged 2 to 5 years), and ﬁnds matches

with the most representative features of ASD deter-

mined and analyzed to date (Talero-Guti

errez et al.,

2012). Similarly, with the hybrid recommendation

system (C¸ ano and Morisio, 2017), the application rec-

ommends to the end-user (i.e., HcP) if it is considered

necessary to carry out specialized tests and referral to

specialists to obtain an ofﬁcial and structured diagno-

sis or to support a related diagnostic process— a gen-

eral conceptual design of GesTEApp is represented in

Figure 1.

We intended to implement GesTEApp in the ﬁrst

stage of the medical process— in the mandatory pedi-

atric controls. This is because we preliminary looked

forward to detecting the ﬁrst signs of the disorder

(i.e., ASD) from the ﬁrst medical approach and avoid

encountering long waiting times for a ﬁrst detection

evaluation.

The detection through behaviors of spectrums,

such as ASD, lets us reﬂect on technological tools

supported by visual (i.e., the recognition of what is

perceived) and less methodical procedures, providing

a less traditional and subjective observation by treat-

ing physicians towards movements and actions of the

patient. Artiﬁcial vision (i.e., computer vision) mod-

els have proven to be effective and helpful in identi-

fying attention and focus behavior patterns and cap-

turing reactions in people with ASD when faced with

visual stimuli.

With GesTEApp, we intended to present the re-

sults obtained in ASD tests through real-time images

and screenshots of the patient’s reaction when the

web-based tool captured the emotion in time intervals.

We meant to visually communicate to the HcP the pa-

tient’s response and allow medical contrasts concern-

ing the expected emotion in a particular time interval.

Also, given the automated and customized recom-

mendation of possible detection of ASD in children,

GesTEApp permits the recommendation’s validation

and supports particular medical decision-making pro-

cesses. Therefore, the evaluation of the proposed

technological solution depended principally on: (1)

time to reduce the ASD detection times compared to

the ordinary medical detection process, and (2) the

accuracy of the hybrid recommendation model.

We focused GesTEApp on HcP, which allows

them to apply standardized ASD tests on the web-

based platform and support their medical analysis on

children (i.e., patients). The solution consists of ﬁve

modules. (1) Test creation module: Allows the cre-

ation of a new test for a patient. (2) Patient creation

module: Allows the creation of new patients in the

system. (3) Patient consultation module: Allows the

consultation and modiﬁcation of a patient’s informa-

tion previously created in the system. (4) Test detail

module: Allows observing the evaluated test results,

the patient’s information, and the information by time

interval. Also, it lets the HcP perform the test qual-

iﬁcation. (5) Test rating module: The HcP can enter

a rating for a test performed through numerical and

open-ended feedback (see Figure 2).

The hybrid recommendation system consisted of

the conjunction of (1) a Content-based Recommenda-

tion System and (2) a Collaborative Filtering Recom-

mendation System. The Content-based Recommender

used a syntactic analysis of the information in the

database related to the test and the user, such as the

results of dominant emotion, response rate range, and

patient age and gender. On the other hand, the Collab-

orative Filtering Recommender considered the rating

given to the tests, fed by the sentiment— extracted

from a sentiment analysis (Zunic et al., 2020) applied

to the observations generated by the HcP. We evalu-

ated the sentiment analysis on three levels of analysis:

(1) Negative, (2) Neutral, and (3) Positive. The re-

sulting hybrid recommendation system is constantly

consulted through web services (See Figure 3).

3 DATA ACQUISITION

The participants who contributed to the experimenta-

tion had roles of HcP and children aged 2 to 5 years.

Each HcP performed speciﬁc tasks in the web-based

solution in this pilot study. Six (N=6) HcPs partic-

ipated in the experimentation phase interacting with

the tool and requesting feedback. In addition, four

(N=4) clinically healthy children between 2 and 5

years participated in the data acquisition and observed

behavior against GesTEApp.

The HcPs completed a questionnaire regarding the

web-based application and how much they recom-

mended GesTEApp. We evaluated this approach fol-

lowing pre-post experimentation. We sought to an-

swer how the platform supported and complemented

early detection of ASD in children through gestural

analysis and customization through a hybrid recom-

mendation system. Hence, we designed a question-

naire to obtain results on the Perception of Usability,

User Experience (UX), and feedback before and when

interacting with the system. We based the question-

naire on the SUMI-type questionnaires (i.e., Software

ICT4AWE 2023 - 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health

172

Figure 2: Features/Functionalities of GesTEApp ( customization features).

Figure 3: Features/Functionalities of GesTEApp (preliminary customization features).

GesTEApp: A Pilot Study on an Expert Web-Based System that Integrates Gestural Analytics and a Hybrid Recommendation System to

Support the Early Detection of ASD in Children

173

Usability Measurement Inventory), which allow the

evaluation of a set of software from the point of view

of the end user (Mansor et al., 2012). The question-

naire considered Open-Ended, Y/N, and (0-5) Likert-

scale (Joshi et al., 2015) responses (See Table 1).

4 EXPERIMENTAL APPROACH

We implemented GesTEApp in a real-world setting,

and to understand the sociocultural transition that oc-

curred in a particular context, we examined the (1)

Habits, (2) Beliefs, (3) Artifacts, and (4) Means of

the actors involved (i.e., HcP and Patients). These

components for analysis are part of the Biopsychoso-

cial and Cultural Systemic Model (BPSCM) intro-

duced by Universidad El Bosque, Colombia (Cruz

and Buitrago, 2017). This model helped us compre-

hend the situation, and we looked forward to enhanc-

ing complexity in this ﬁeld. Furthermore, this model

could describe how its components may systemically

change if the participants engage with the web-based

tool.

Therefore, we evaluated GesTEApp carrying out a

pilot study on ﬁve patients (N=5) and six HcP (N=6).

We assessed the interaction of the web-based tool

with a sample of 5 individuals from the target pop-

ulation (i.e., children between 2 and 5 years old). We

looked forward to observing and evaluating its appli-

cability, reception, and behavior against the solution.

Following the Technology Transfer Cycle (TTC)

(Gorschek et al., 2006), we followed a case study

strategy. HcP principally validated GesTEApp to ob-

tain feedback on the web-based tool. In addition,

this strategy made it possible to validate both qual-

itative and quantitative variables, allowing the sys-

tem to be observed from a broader vision, evaluat-

ing and understanding the performance of GesTEApp

from the variables “time” and “accuracy of detection”

-quantitative- and “support to the health” -qualitative.

We designed and asked HcPs to respond to a ques-

tionnaire after interacting with GesTEApp to obtain

their feedback and perception of usability, user expe-

rience, and times involved. Moreover, we applied the

observation method (Ciesielska et al., 2018), which

contributed us to evaluate the perception of the in-

teraction of the actors (i.e., HcP and patients) with

GesTEApp without intervention or external help, al-

lowing us to support the validation of the user expe-

rience, usability, preliminary detection times and its

applicability in particular contexts.

We validated the questionnaire using an inter-

agreement measure (Jolivald et al., 2022). We consid-

ered the calculation of the Intra-class Correlation Co-

efﬁcient (ICC) with three (N=3) raters for the 14-item

questionnaire (see Table 1). We followed the stan-

dardized process for the calculation: (1) Each rater

evaluated all the questions following a 5-point Likert-

scale (Joshi et al., 2015); (2) calculated the between-

group sum of squares across all raters; (3) calculate

the within-group sum of squares across all items and

item-based ratings; and (4) considered the formula for

ICC(2,3) to calculate the ICC. We obtained an ICC

of 0.83 (i.e., 83% of variance), which means that the

questions posed in the questionnaire were considered

consistent and reliable— an ICC of 0.75 or higher in-

dicates good to excellent agreement.

Likewise, we proposed the following general vali-

dation steps: (1) Find the environment where the case

study would be carried out (e.g., foundation, clinic,

among others), (2) Establish interviews with expert

and non-expert HcP in the area, who evaluate the op-

eration and coherence of the technological tool from

their medical perspective through a questionnaire. In

addition, make an observation method towards the

use of the tool by the HcP, allowing us to evaluate

the HcP’s perception of usability and user experience

(UX) of the web-based tool, and (3) Execution of a

Test Plan for the validation phase of GesTEApp.

5 FINDINGS AND RESULTS

The ability of GesTEApp to be understood, used,

learned, and visibly attractive to the HcP was eval-

uated. As a result, 100% of the HcP agreed with the

ease of use of the tool. On the other hand, 60% of HcP

positively considered the probability of rapid learning

to use the tool by HcP.

Regarding the user experience validation towards

the HcP’s perception of GesTEApp, we obtained that

80% of the HcP considered that the solution provided

easy navigation. Also, 80% believed the informa-

tion on the test result provided by the tool was sufﬁ-

cient. Moreover, 80% would be willing to implement

this tool frequently, and 50% of HcPs considered its

graphic interface very pleasant.

We could obtain the following preliminary ob-

servations from the validation with the child popu-

lation. First, some children responded more expres-

sively than others to the video. We could observe

that this depended on their age and the environment

in which the test was applied. This suggests the need

to locate the child (i.e., patient) in controlled envi-

ronments— for example, the camera should focus di-

rectly on the child’s face instead of the complete body.

Also, it is necessary to eliminate any distractions that

could interfere with the application of the test. More-

ICT4AWE 2023 - 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health

174

Table 1: Questionnaire regarding the pre-post experience of HcPs interacting with GesTEApp.

Question Type of Question:

(1)

, OE

(2)

, LS

(3)

or YN

(4)

Moment of

Application:

(5)

or A

(6)

Q1: Provide your age range. MC B

Q2: Which is your current job title? OE B

Q3: Which is your professional specialty in health? OE B

Q4: Indicate if you have worked on patients with ASD. YN B

Q5: The Graphical User Interface (GUI) of GesTEApp is easy to

use.

LS A

Q6: Indicate how you consider the invested time of the ASD test

to be.

LS A

Q7: GesTEApp could be implemented within the periodic

checkups for pediatric development control.

LS A

Q8: HcPs would quickly learn to use GesTEApp. LS A

Q9: How easily do you consider navigation through the different

components of GesTEApp?

LS A

Q10: Indicate how you consider to be the supplied information

of the test result of GesTEApp.

LS A

Q11: How visually pleasing is the graphical interface of

GesTEApp?

LS A

Q12: How willing would you feel to use this tool frequently? LS A

Q13: How useful do you consider GesTEApp in supporting the

early detection of Autism Spectrum Disorder in children from 2

to 5 years of age?

LS A

Q14: General feedback on the tool (Aspects to

improve/opinions/suggestions)

OE A

Notes:

(1)

MC: Multiple Choice,

(2)

OE: Open-Ended Question,

(3)

LS: [0-5] Likert Scale,

(4)

: YN:Y/N Questions,

(5)

B: Before using GesTEApp,

(6)

A: After using GesTEApp

over, we also observed that most children showed in-

terest in the emotion-inducing video, which is consid-

ered a signiﬁcant achievement for this pilot study—

the emotion-inducing video was included in the case-

based experimentation approach.

On the other hand, we can also assert that there is

no instance of overﬁtting (Ying, 2019) or underﬁtting

(Sehra et al., 2021) if we acquire an F1-Score of be-

tween 70% and 85% (i.e., we achieved 71%). Also,

the speciﬁcity resulted in 100%— the speciﬁcity is

understood as the negative cases that the algorithm

correctly classiﬁed.

Regarding the time it takes to carry out the en-

tire ﬂow of the process with GesTEApp by the HcP,

from the beginning of the session until the result is ob-

tained (i.e., recommendation), we conducted ten test

scenarios (N=10) to evaluate the duration of the whole

test process. As a result, GesTEApp reached an av-

erage time of 8.4 minutes, including taking the test

and generating the result, with a standard deviation of

1.57, demonstrating a low dispersion of the data and

stability concerning the execution times of the test—

representing a 36% reduction compared to traditional

tools.

Although the symptoms and behavior of ASD

are naturally varied and heterogeneous, which leads

to considering it a spectrum (Masi et al., 2017), it

is possible to take into account some patterns that

can be used to support detection procedures through

automated recommendations. This is evidenced in

GesTEApp, where patterns of facial expressions were

considered to generate personalized recommenda-

tions regarding ASD in children.

Moreover, from the feedback and perceptions

of the HcPs, the recommendations that GesTEApp

provides support medical decision-making processes,

and promote a deepened medical analysis in their pro-

cedures to achieve early detection of ASD in chil-

dren, as intended with the solution proposed and ana-

lyzed in this paper. Also, we found that it is possible

to support an early detection medical procedure on

ASD from its ﬁrst manifestations and by implement-

ing technological tools such as GesTEApp.

6 DISCUSSION

From the preliminary results of this pilot study, we

can claim that GesTEApp could positively generate

GesTEApp: A Pilot Study on an Expert Web-Based System that Integrates Gestural Analytics and a Hybrid Recommendation System to

Support the Early Detection of ASD in Children

175

a probability of suffering from ASD in children—

quantitative support. This helps the HcP in decision-

making, capturing and analyzing primary manifesta-

tions of ASD based on difﬁculties or ability to express

emotions, giving them extra support for much more

objective medical decision-making processes. There-

fore, GesTEApp is an expert system with case-based

reasoning because it proposes a possible probability

of the suffering of ASD in children from the compar-

ison with similar tests extracted from a case base.

According to the literature, children with ASD

tend to have difﬁculty processing the basic emotions

of disgust, anger, and surprise (Smith et al., 2010).

On the other hand, children with ASD have some dif-

ﬁculties in recognizing and expressing the emotion of

surprise due to two main factors: First, since the ex-

pression of surprise involves the decoding of the ex-

pression of the eyes and mouth, unlike the emotion of

“happiness” and “sadness” that can be decoded sim-

ply through the mouth. The second reason is justiﬁed

by how complex the emotion of “surprise” can be-

come because this expression can usually mix more

than one emotion, thus making it difﬁcult for children

with ASD to understand and replicate it.

Therefore, we opted to calculate and analyze a

percentage probability of suffering from ASD within

a detection trafﬁc light (Omachi and Omachi, 2009).

We considered green between 0% to 33% probability

of detection of ASD, yellow (+) between 34% to 50%,

yellow (-) between 51% to 66%, and red between 67%

to 100%.

GesTEApp successfully offers a friendly and user-

centered web-based solution to support HcPs in the

early detection of ASD in children, according to the

preliminary results and comments obtained of this pi-

lot study. Additionally, we discovered that the par-

ticipants’ feedback on their web-based platform ex-

perience was favorable and very helpful to what we

intended with the solution, which let us use their sug-

gestions to improve the tool for subsequent iterations.

The expert system integrated two main compo-

nents: (1) an inference engine and (2) a case base.

The case base contained information on the previ-

ously resolved problems— it included the tests that

have already been carried out, analyzed, and evalu-

ated by the system with feedback from the HcP. On

the other hand, the inference engine (Naykhanova and

Naykhanova, 2018) compared the inserted problem

(i.e., the test to be evaluated) with the problems stored

and solved in the case base to obtain a result that

meets the highest degree of similarity.

All HcP (i.e., participants of the pilot study) who

interacted with GesTEApp agreed regarding the tool’s

ease of use. Furthermore, 60% of them positively

consider the possibility of rapid curve learning using

the tool.

Additionally, regarding user experience validation

of the web-based tool, we evaluated the HcP’s per-

ception and response to GesTEApp. In the ﬁrst place,

80% of HcPs considered navigation through the ap-

plication modules easy and the information from the

test result provided by the tool sufﬁcient and would be

willing to implement this tool frequently in their med-

ical procedures. According to the Harris Poll con-

ducted in collaboration with Stanford University, 55%

of HcP are willing to experiment with and adopt new

technologies provided by other HcPs, who have previ-

ously utilized and approved them (Wuerdeman et al.,

2005). Therefore, 80% of the participants considered

the tool’s graphical interface (GUI) friendly, despite

20% of the participants who considered it not very

pleasant. Moreover, compared to GesTEApp, tradi-

tional tools take an average of 15 minutes to apply a

detection test, in addition to the duration of evaluation

of the responses and analysis by the HcP to obtain a

ﬁnal result.

Moreover, we can also claim that it is possible to

support HcPs in detecting ASD (i.e., Autism Spec-

trum Disorder) through an expert system based on a

hybrid recommendation system that implements ges-

tural analytics models that manage to capture and ana-

lyze the facial expressions of children. This tool con-

tributes to understanding behaviors and emotions in

the diverse diagnoses used by HcP to detect this spec-

trum disorder. Therefore, we can preliminary state

with this pilot study that a tool such as GesTEApp

supports this idea. Nevertheless, further research on

the web-based tool proposed to support HcPs for early

detection processes of ASD in children will be carried

out to substantiate and fulﬁll all assertions discussed

in this document.

ACKNOWLEDGEMENTS

The authors very specially thank Santiago Andr

Bedoya-Rodr

ıguez for his contribution, high perfor-

mance, and dedication in this pilot study, which was

essential to obtaining the data, ﬁndings, and out-

comes described in this report. Likewise, the au-

thors thank all participants who voluntarily and ac-

tively contributed to validating GesTEApp.

REFERENCES

C¸ ano, E. and Morisio, M. (2017). Hybrid recommender

systems: A systematic literature review. Intelligent

ICT4AWE 2023 - 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health

176

Data Analysis, 21(6):1487–1524.

Ciesielska, M., Bostr

om, K. W., and

Ohlander, M. (2018).

Observation methods. In Qualitative methodologies

in organization studies, pages 33–52. Springer.

Coelho-Medeiros, M. E., Bronstein, J., Aedo, K., Pereira,

J. A., Arra

no, V., Perez, C. A., Valenzuela, P. M.,

Moore, R., Garrido, I., and Bedregal, P. (2019). M-

chat-r/f validation as a screening tool for early detec-

tion in children with autism spectrum disorder. Re-

vista chilena de pediatria, 90(5):492–499.

Cruz, O. L. and Buitrago, C. H. O. (2017). Formaci

on de

ingenieros inform

aticos como gestores de innovaci

on:

Transformadores de vidas, constructores de futuros

posibles. ANFEI Digital, (7).

Dumont-Mathieu, T. and Fein, D. (2005). Screening for

autism in young children: The modiﬁed checklist

for autism in toddlers (m-chat) and other measures.

Mental retardation and developmental disabilities re-

search reviews, 11(3):253–262.

Edition, F. et al. (2013). Diagnostic and statistical manual of

mental disorders. Am Psychiatric Assoc, 21(21):591–

643.

Eysenbach, G. et al. (2001). What is e-health? Journal of

medical Internet research, 3(2):e833.

Goin-Kochel, R. P., Mackintosh, V. H., and Myers, B. J.

(2006). How many doctors does it take to make an

autism spectrum diagnosis? Autism, 10(5):439–451.

Gorschek, T., Garre, P., Larsson, S., and Wohlin, C. (2006).

A model for technology transfer in practice. IEEE

software, 23(6):88–95.

Jolivald, A., Yarnell, K., Hall, C., and Ijichi, C. (2022). Do

you see what i see? investigating the validity of an

equine personality questionnaire. Applied Animal Be-

haviour Science, 248:105567.

Joshi, A., Kale, S., Chandel, S., and Pal, D. K. (2015). Lik-

ert scale: Explored and explained. British journal of

applied science & technology, 7(4):396.

Lai, M.-C., Lombardo, M. V., and Baron-Cohen, S. (2014).

Autism. Lancet, 383(9920):896–910.

Lord, C., Brugha, T. S., Charman, T., Cusack, J., Dumas,

G., Frazier, T., Jones, E. J., Jones, R. M., Pickles, A.,

State, M. W., et al. (2020). Autism spectrum disorder.

Nature reviews Disease primers, 6(1):1–23.

Mansor, Z., Kasirun, Z. M., Yahya, S., and Arshad, N. H.

(2012). The evaluation of webcost using software us-

ability measurement inventory (sumi). International

Journal of Digital Information and Wireless Commu-

nications, 2(2):197–201.

Masi, A., DeMayo, M. M., Glozier, N., and Guastella, A. J.

(2017). An overview of autism spectrum disorder, het-

erogeneity and treatment options. Neuroscience bul-

letin, 33(2):183–193.

Naykhanova, L. and Naykhanova, I. (2018). Concep-

tual model of knowledge base system. In Journal

of Physics: Conference Series, volume 1015, page

032097. IOP Publishing.

of Health, C. M. (2015). Protocolo cl

ınico para el di-

agn

ostico, tratamiento y ruta de atenci

on integral de

nos y ni

nas con trastornos del espectro autista.

Omachi, M. and Omachi, S. (2009). Trafﬁc light detec-

tion with color and edge information. In 2009 2nd

IEEE International Conference on Computer Science

and Information Technology, pages 284–287. IEEE.

Prentice, C., Dominique Lopes, S., and Wang, X. (2020).

Emotional intelligence or artiﬁcial intelligence–an

employee perspective. Journal of Hospitality Market-

ing & Management, 29(4):377–403.

Roman-Urrestarazu, A., Y

nez, C., L

opez-Gar

ı, C.,

Elgueta, C., Allison, C., Brayne, C., Troncoso, M.,

and Baron-Cohen, S. (2021). Autism screening and

conditional cash transfers in chile: Using the quanti-

tative checklist (q-chat) for early autism detection in a

low resource setting. Autism, 25(4):932–945.

Sehra, S., Flores, D., and Montanez, G. D. (2021). Undecid-

ability of underﬁtting in learning algorithms. In 2021

2nd International Conference on Computing and Data

Science (CDS), pages 591–594. IEEE.

Seth, R. and Sharaff, A. (2022). A comparative overview

of hybrid recommender systems: Review, challenges,

and prospects. Data Mining and Machine Learning

Applications, pages 57–98.

Smith, M. J. L., Montagne, B., Perrett, D. I., Gill, M., and

Gallagher, L. (2010). Detecting subtle facial emotion

recognition deﬁcits in high-functioning autism using

dynamic stimuli of varying intensities. Neuropsy-

chologia, 48(9):2777–2781.

Spitzer, R. L., Md, K. K., and Williams, J. B. (1980). Di-

agnostic and statistical manual of mental disorders. In

American psychiatric association. Citeseer.

Talero-Guti

errez, C., Rodr

ıguez, M., De La Rosa, D.,

Morales, G., and V

elez-Van-Meerbeke, A. (2012).

Proﬁle of children and adolescents with autism spec-

trum disorders in an institution in bogota, colombia.

Neurolog

ıa (English Edition), 27(2):90–96.

Wuerdeman, L., Volk, L., Pizziferri, L., Tsurikova, R., Har-

ris, C., Feygin, R., Epstein, M., Meyers, K., Wald,

J. S., Lansky, D., et al. (2005). How accurate is in-

formation that patients contribute to their electronic

health record? In AMIA annual symposium proceed-

ings, volume 2005, page 834. American Medical In-

formatics Association.

Ying, X. (2019). An overview of overﬁtting and its solu-

tions. In Journal of physics: Conference series, vol-

ume 1168, page 022022. IOP Publishing.

Zunic, A., Corcoran, P., Spasic, I., et al. (2020). Sentiment

analysis in health and well-being: systematic review.

JMIR medical informatics, 8(1):e16023.

GesTEApp: A Pilot Study on an Expert Web-Based System that Integrates Gestural Analytics and a Hybrid Recommendation System to

Support the Early Detection of ASD in Children

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